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Scale bridging materials physics: Active learning workflows and
  integrable deep neural networks for free energy function representations in
  alloys
v1v2v3v4v5 (latest)

Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys

Computer Methods in Applied Mechanics and Engineering (CMAME), 2020
30 January 2020
G. Teichert
A. Natarajan
Anton Van der Ven
K. Garikipati
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys"

6 / 6 papers shown
Physics- and data-driven Active Learning of neural network representations for free energy functions of materials from statistical mechanics
Physics- and data-driven Active Learning of neural network representations for free energy functions of materials from statistical mechanics
Jamie Holber
Krishna Garikipati
AI4CE
275
0
0
25 Feb 2025
Machine Learning in Heterogeneous Porous Materials
Machine Learning in Heterogeneous Porous Materials
Martha DÉli
H. Deng
Cedric G. Fraces
K. Garikipati
L. Graham‐Brady
...
H. Tchelepi
B. Važić
Hari S. Viswanathan
H. Yoon
P. Zarzycki
AI4CE
240
12
0
04 Feb 2022
A heteroencoder architecture for prediction of failure locations in
  porous metals using variational inference
A heteroencoder architecture for prediction of failure locations in porous metals using variational inferenceComputer Methods in Applied Mechanics and Engineering (CMAME), 2022
Wyatt Bridgman
Xiaoxuan Zhang
G. Teichert
M. Khalil
K. Garikipati
Reese E. Jones
UQCVAI4CE
247
6
0
31 Jan 2022
Predicting Mechanically Driven Full-Field Quantities of Interest with
  Deep Learning-Based Metamodels
Predicting Mechanically Driven Full-Field Quantities of Interest with Deep Learning-Based MetamodelsExtreme Mechanics Letters (Extreme Mech. Lett.), 2021
S. Mohammadzadeh
Emma Lejeune
AI4CE
225
36
0
24 Jul 2021
Li$_x$CoO$_2$ phase stability studied by machine learning-enabled scale
  bridging between electronic structure, statistical mechanics and phase field
  theories
Lix_xx​CoO2_22​ phase stability studied by machine learning-enabled scale bridging between electronic structure, statistical mechanics and phase field theories
G. Teichert
Sambit Das
Muratahan Aykol
C. Gopal
V. Gavini
K. Garikipati
101
2
0
16 Apr 2021
Bayesian neural networks for weak solution of PDEs with uncertainty
  quantification
Bayesian neural networks for weak solution of PDEs with uncertainty quantification
Xiaoxuan Zhang
K. Garikipati
AI4CE
253
15
0
13 Jan 2021
1
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